3D Tracking Using Multi-view Based Particle Filters

  • Raúl Mohedano
  • Narciso García
  • Luis Salgado
  • Fernando Jaureguizar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5259)


Visual surveillance and monitoring of indoor environments using multiple cameras has become a field of great activity in computer vision. Usual 3D tracking and positioning systems rely on several independent 2D tracking modules applied over individual camera streams, fused using geometrical relationships across cameras. As 2D tracking systems suffer inherent difficulties due to point of view limitations (perceptually similar foreground and background regions causing fragmentation of moving objects, occlusions), 3D tracking based on partially erroneous 2D tracks are likely to fail when handling multiple-people interaction. To overcome this problem, this paper proposes a Bayesian framework for combining 2D low-level cues from multiple cameras directly into the 3D world through 3D Particle Filters. This method allows to estimate the probability of a certain volume being occupied by a moving object, and thus to segment and track multiple people across the monitored area. The proposed method is developed on the basis of simple, binary 2D moving region segmentation on each camera, considered as different state observations. In addition, the method is proved well suited for integrating additional 2D low-level cues to increase system robustness to occlusions: in this line, a naïve color-based (HSI) appearance model has been integrated, resulting in clear performance improvements when dealing with complex scenarios.


Particle Filter Multiple Camera Motion Segmentation Visual Surveillance Multiple People 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T.: A tutorial on Particle Filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Processing 50(2), 174–188 (2002)CrossRefGoogle Scholar
  2. 2.
    Black, J., Ellis, T., Rosin, P.: Multi view image surveillance and tracking. In: Proc. Workshop on Motion and Video Computing, pp. 169–174 (2002)Google Scholar
  3. 3.
    Doucet, A., Godsill, S.J., Andrieu, C.: On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing 10(3), 197–208 (2000)CrossRefGoogle Scholar
  4. 4.
    Fleuret, F., Berclaz, J., Lengagne, R., Fua, P.: Multicamera people tracking with a probabilistic occupancy map. IEEE Trans. Pattern Analysis and Machine Intelligence 30(2), 267–282 (2008)CrossRefGoogle Scholar
  5. 5.
    Focken, D., Stiefelhagen, R.: Towards vision-based 3-D people tracking in a smart room. In: Proc. IEEE Int. Conf. Multimodal Interfaces, pp. 400–405 (2002)Google Scholar
  6. 6.
    Gonzalez, R.I., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice Hall, Englewood Cliffs (2008)Google Scholar
  7. 7.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2004)CrossRefzbMATHGoogle Scholar
  8. 8.
    Hu, W., Tan, T., Wang, L., Maybank, S.: A survey on visual surveillance of object motion and behaviors. IEEE Trans. Systems, Man, and Cybernetics 34(3), 334–352 (2004)CrossRefGoogle Scholar
  9. 9.
    Isard, M., Blake, A.: CONDENSATION - Conditional Density Propagation for visual tracking. Int. J. Computer Vision 29(1), 5–28 (1998)CrossRefGoogle Scholar
  10. 10.
    Krumm, J., Harris, S., Meyers, B., Brumitt, B., Hale, M.: Multi-camera multi-person tracking for easy living. In: IEEE Int. Workshop on Visual Surveillance (2000)Google Scholar
  11. 11.
    Landabaso, J.L., Pardás, M.: Foreground regions extraction and characterization towards real-time object tracking. In: Renals, S., Bengio, S. (eds.) MLMI 2005. LNCS, vol. 3869, pp. 241–249. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Raúl Mohedano
    • 1
  • Narciso García
    • 1
  • Luis Salgado
    • 1
  • Fernando Jaureguizar
    • 1
  1. 1.Grupo de Tratamiento de ImágenesUniversidad Politécnica de MadridMadridSpain

Personalised recommendations